In this study, the effectiveness of 15 global climate models (GCMs) for simulating weather data of Rasht synoptic station in the north of Iran was evaluated using a statistical downscaling approach. Downscaling of GCMs was performed using a stochastic weather generator model (LARS-WG5.5) and the best GCM (INCM3) was selected. The parameters such as precipitation, radiation, temperature and reference evapotranspiration were simulated using the selected GCMs for two periods of 2013–2042 and 2043–2072, and accordingly, the rice water requirement was estimated for the coming periods. Then, simulated results were compared with data in the baseline period (1981–2010). The results showed that reference evapotranspiration (ETo) for all the seasons will increase in the coming periods. The highest ETo increase (18.5–23.7 mm month–1) will occur in the spring. Also, the average rice water requirement will increase between 178 and 572 m3 ha–1 depending on the emission scenarios and future studied periods. The incremental changes in ETo and, consequently, in rice water requirement for the coming periods will occur as a result of the significant increase in temperature. The results of this study can be used by local planners as a correct view of water demand in the future.

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